
# Importing necessary libraries

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
from numpy import dtype
import random
from pandas.core.tools.datetimes import to_datetime
from scipy import stats
from scipy.stats import shapiro
from sklearn.preprocessing import LabelEncoder
from sklearn.metrics import mean_squared_error


# This code disables the display of warning messages to the console.

import warnings
warnings.filterwarnings("ignore")


data = pd.read_csv('Crime_Data_from_2020_to_Present.csv')

df = pd.DataFrame(data)

df


# Display the first few rows of the dataset

df.head()


# Check the data types of each column

df.dtypes

DR_NO               int64
Date Rptd          object
DATE OCC           object
TIME OCC            int64
AREA                int64
AREA NAME          object
Rpt Dist No         int64
Part 1-2            int64
Crm Cd              int64
Crm Cd Desc        object
Mocodes            object
Vict Age            int64
Vict Sex           object
Vict Descent       object
Premis Cd         float64
Premis Desc        object
Weapon Used Cd    float64
Weapon Desc        object
Status             object
Status Desc        object
Crm Cd 1          float64
Crm Cd 2          float64
Crm Cd 3          float64
Crm Cd 4          float64
LOCATION           object
Cross Street       object
LAT               float64
LON               float64
dtype: object


df.columns

Index(['DR_NO', 'Date Rptd', 'DATE OCC', 'TIME OCC', 'AREA', 'AREA NAME',
       'Rpt Dist No', 'Part 1-2', 'Crm Cd', 'Crm Cd Desc', 'Mocodes',
       'Vict Age', 'Vict Sex', 'Vict Descent', 'Premis Cd', 'Premis Desc',
       'Weapon Used Cd', 'Weapon Desc', 'Status', 'Status Desc', 'Crm Cd 1',
       'Crm Cd 2', 'Crm Cd 3', 'Crm Cd 4', 'LOCATION', 'Cross Street', 'LAT',
       'LON'],
      dtype='object')


df.describe()


df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 807377 entries, 0 to 807376
Data columns (total 28 columns):
 #   Column          Non-Null Count   Dtype  
---  ------          --------------   -----  
 0   DR_NO           807377 non-null  int64  
 1   Date Rptd       807377 non-null  object 
 2   DATE OCC        807377 non-null  object 
 3   TIME OCC        807377 non-null  int64  
 4   AREA            807377 non-null  int64  
 5   AREA NAME       807377 non-null  object 
 6   Rpt Dist No     807377 non-null  int64  
 7   Part 1-2        807377 non-null  int64  
 8   Crm Cd          807377 non-null  int64  
 9   Crm Cd Desc     807377 non-null  object 
 10  Mocodes         696010 non-null  object 
 11  Vict Age        807377 non-null  int64  
 12  Vict Sex        701468 non-null  object 
 13  Vict Descent    701460 non-null  object 
 14  Premis Cd       807368 non-null  float64
 15  Premis Desc     806901 non-null  object 
 16  Weapon Used Cd  281174 non-null  float64
 17  Weapon Desc     281174 non-null  object 
 18  Status          807377 non-null  object 
 19  Status Desc     807377 non-null  object 
 20  Crm Cd 1        807367 non-null  float64
 21  Crm Cd 2        59483 non-null   float64
 22  Crm Cd 3        1987 non-null    float64
 23  Crm Cd 4        58 non-null      float64
 24  LOCATION        807377 non-null  object 
 25  Cross Street    129232 non-null  object 
 26  LAT             807377 non-null  float64
 27  LON             807377 non-null  float64
dtypes: float64(8), int64(7), object(13)
memory usage: 172.5+ MB


# Identifying missing data

df.isna().sum()

DR_NO                  0
Date Rptd              0
DATE OCC               0
TIME OCC               0
AREA                   0
AREA NAME              0
Rpt Dist No            0
Part 1-2               0
Crm Cd                 0
Crm Cd Desc            0
Mocodes           111367
Vict Age               0
Vict Sex          105909
Vict Descent      105917
Premis Cd              9
Premis Desc          476
Weapon Used Cd    526203
Weapon Desc       526203
Status                 0
Status Desc            0
Crm Cd 1              10
Crm Cd 2          747894
Crm Cd 3          805390
Crm Cd 4          807319
LOCATION               0
Cross Street      678145
LAT                    0
LON                    0
dtype: int64


# Dropping the columns which are having more than 80% missing data
# Filling object columns with mode of column
# Filling Numeric columns with mean of column

obj_cols = ['Mocodes', 'Vict Sex', 'Vict Descent', 'Premis Desc', 'Weapon Desc']
drop_cols = ['Crm Cd 2', 'Crm Cd 3', 'Crm Cd 4', 'Cross Street']
num_cols = ['Premis Cd', 'Weapon Used Cd', 'Crm Cd 1']

df.drop(drop_cols, axis = 1, inplace = True)

for i in obj_cols:
    mode_value = df[i].mode()[0]
    print(mode_value)
    df[i].fillna(mode_value, inplace = True)

for i in num_cols:
    df[i].fillna(df[i].mean(), inplace = True)

0344
M
H
STREET
STRONG-ARM (HANDS, FIST, FEET OR BODILY FORCE)


# Checking if there is any other missing data

df.isna().sum()

DR_NO             0
Date Rptd         0
DATE OCC          0
TIME OCC          0
AREA              0
AREA NAME         0
Rpt Dist No       0
Part 1-2          0
Crm Cd            0
Crm Cd Desc       0
Mocodes           0
Vict Age          0
Vict Sex          0
Vict Descent      0
Premis Cd         0
Premis Desc       0
Weapon Used Cd    0
Weapon Desc       0
Status            0
Status Desc       0
Crm Cd 1          0
LOCATION          0
LAT               0
LON               0
dtype: int64


# Drpooing the duplicates from dataframe

df.drop_duplicates()


# Convert data types

dates = ['Date Rptd', 'DATE OCC']
times = ['TIME OCC']
int_type = ['Weapon Used Cd', 'Premis Cd', 'Crm Cd 1']



for i in dates:
  df[i] = to_datetime(df[i], format = 'mixed')

for i in times:
  df[i] = df[i].astype(str).str.zfill(4)
  df[i] = to_datetime(df[i], format = '%H%M').dt.time

for i in int_type:
  df[i] = df[i].astype(np.int64)

df.dtypes

DR_NO                      int64
Date Rptd         datetime64[ns]
DATE OCC          datetime64[ns]
TIME OCC                  object
AREA                       int64
AREA NAME                 object
Rpt Dist No                int64
Part 1-2                   int64
Crm Cd                     int64
Crm Cd Desc               object
Mocodes                   object
Vict Age                   int64
Vict Sex                  object
Vict Descent              object
Premis Cd                  int64
Premis Desc               object
Weapon Used Cd             int64
Weapon Desc               object
Status                    object
Status Desc               object
Crm Cd 1                   int64
LOCATION                  object
LAT                      float64
LON                      float64
dtype: object

df


# Normalizing data using min-max normalization

# This function normalizes the values in a given column to the range [0, 1].
# It takes the input 'column' and scales its values so that the minimum value becomes 0 and the maximum value becomes 1.

def norm(column):
  # Calculate the range of values in the column (max - min).
  max_min = column.max() - column.min()
  return (column - column.min()) / max_min


# Normalize numerical data

# Iterate over each column in the DataFrame 'df'.
for i in df.columns:
    # Check if the data type of the column is np.int64 (64-bit integer).
  if df[i].dtype == np.int64:
    # Perform the Shapiro-Wilk normality test on the column.
    stat, p_value = shapiro(df[i])
    # Check if the p-value from the Shapiro-Wilk test is less than 0.05 (a common significance level).
    if p_value < 0.05:
      print(i)
      df[i] = norm(df[i]) # Apply a normalizing transformation to the column.

DR_NO
AREA
Rpt Dist No
Part 1-2
Crm Cd
Vict Age
Premis Cd
Weapon Used Cd
Crm Cd 1


### Encoding data

df_enc = df.copy()

li = ['AREA NAME', 'Crm Cd Desc', 'Mocodes', 'Vict Sex', 'Vict Descent', 'Premis Desc', 'Weapon Desc', 'Status', 'Status Desc', 'LOCATION']

# Initialize a LabelEncoder instance.
le = LabelEncoder()
for i in li:
  df_enc[i] = le.fit_transform(df_enc[i])

# The 'df_enc' DataFrame now contains the original data with the specified columns encoded as numerical values.
df_enc


# Extract the year and month from the 'DATE OCC' column and add them as new columns to the DataFrame 'df'.

df['occ_year'] = df['DATE OCC'].dt.year
df['occ_month'] = df['DATE OCC'].dt.month


# Group the DataFrame by the year of occurrence and sum the 'DR_NO' column to get the total number of crimes per year.
crime_per_year = df.groupby('occ_year')['DR_NO'].sum()

# Create a bar plot to visualize the total number of crimes per year.
plt.figure(figsize=(10, 6))
crime_per_year.plot(kind='bar', color='skyblue')
plt.xlabel('Year')
plt.ylabel('Total Number of Crimes')
plt.title('Total Number of Crimes Per Year')
plt.xticks(rotation=45)
plt.show()


# Set the figure size for the plot.
plt.figure(figsize = (10, 6))
plt.title('Crime trends from 2020 to the Present Year')

# Group the DataFrame by the year of occurrence and count the number of crimes in each year.
crime_by_year = df.groupby('occ_year').count().reset_index()

# Create a line plot using Seaborn to show the trend of the number of crimes per year.
sns.lineplot(data = crime_by_year, x = 'occ_year', y = 'Crm Cd Desc')
plt.xlabel('Year')
plt.ylabel('Number of Crimes')
plt.show()


plt.figure(figsize = (10, 6))

# Group the DataFrame by the month of occurrence and count the number of crimes in each month.
crime_by_month = df.groupby('occ_month').count().reset_index()
plt.title('Seasonal pattern in crime data')

# Create a bar plot using Seaborn to show the number of crimes per month.
sns.barplot(data = crime_by_month, x = 'occ_month', y = 'Crm Cd Desc')
plt.xlabel('Month')
plt.ylabel('Number of Crimes')

# Set custom labels for the x-axis (month names).
plt.xticks(range(12), ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'June', 'July', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'])
plt.show()


# Calculate the average number of crimes per month for each year and store the results in 'average_crimes_per_month'.
average_crimes_per_month = df.groupby(['occ_year', 'occ_month'])['DR_NO'].mean()

# Create a DataFrame to hold the results and reset the index
average_crimes_df = average_crimes_per_month.reset_index()

# Plot the results
plt.figure(figsize=(12, 6))

# Iterate through unique years in the DataFrame.
for year in average_crimes_df['occ_year'].unique():
    # Extract data for the current year.
    year_data = average_crimes_df[average_crimes_df['occ_year'] == year]
    
    # Plot the data as a line, using the month as the x-axis and the average number of crimes as the y-axis, with a label indicating the year.
    plt.plot(year_data['occ_month'], year_data['DR_NO'], label=f'Year {year}')

plt.xlabel('Month')
plt.ylabel('Average Number of Crimes')
plt.title('Average Number of Crimes Per Month Over the Years')

# Display a legend indicating the year for each line on the plot.
plt.legend()
plt.xticks(range(1, 13), ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'])
plt.show()


crime_type_counts = data['Crm Cd Desc'].value_counts()

# Identify the crime type with the highest frequency
most_frequent_crime = crime_type_counts.idxmax()  # Get the index (crime type) with the highest count.
highest_frequency = crime_type_counts.max()  # Get the highest count.

print("Crime Type with the Highest Frequency:", most_frequent_crime)
print("Frequency:", highest_frequency)

Crime Type with the Highest Frequency: VEHICLE - STOLEN
Frequency: 86335


# Calculate the frequency of each crime type in the 'Crm Cd Desc' column.
crime_counts = df['Crm Cd Desc'].value_counts()

# Identify the most common crime type with the highest frequency.
most_common_crime = crime_counts.idxmax()

print("Most Common Crime:", most_frequent_crime)

# Create a DataFrame containing data only for the most common crime type.
most_common_crime_data = df[df['Crm Cd Desc'] == most_common_crime]

# Add 'Year' and 'Month' columns to 'most_common_crime_data'.
most_common_crime_data['Year'] = df['occ_year']
most_common_crime_data['Month'] = df['occ_month']

# Group the data by year and month, and count the number of crimes in each period.
crime_trends = most_common_crime_data.groupby(['Year', 'Month'])['DR_NO'].count().reset_index()

plt.figure(figsize=(10, 6))
plt.title("Trends in Most Common Crime Over Time")

# Create a line plot to show the number of crimes over the years.
sns.lineplot(data=crime_trends, x = 'Year', y = 'DR_NO')
plt.xlabel('Year')
plt.ylabel('Number of Crimes')
plt.show()

Most Common Crime: VEHICLE - STOLEN


# Group the data by 'AREA NAME' and count the number of crimes in each region, then reset the index.
crime_by_region = data.groupby('AREA NAME')['DR_NO'].count().reset_index()

# Sort the regions in descending order of crime frequency.
crime_by_region = crime_by_region.sort_values(by = 'DR_NO', ascending=False)

plt.figure(figsize=(10, 6))
plt.title('Crime Rates by Region')

# Create a bar plot to show the number of crimes in each region.
sns.barplot(data=crime_by_region, x='AREA NAME', y='DR_NO')
plt.ylabel('Number of Crimes')
plt.xticks(rotation=45)
plt.show()


# Group the 'df' DataFrame by year and month to count the number of crimes in each period.
crime_data = df.groupby(['occ_year','occ_month']).count()

# Load economic data from a CSV file (assuming it contains economic factors) into a DataFrame.
economic_data = pd.read_csv('economic factors.csv')

# Add a new column 'Crime count' in the 'economic_data' DataFrame and populate it with crime counts from the 'crime_data' DataFrame.
economic_data['Crime count']=crime_data['DR_NO'].reset_index()['DR_NO']


economic_data


# Calculate the correlation matrix between economic factors and the crime count.
correlation=economic_data[['Unemployment Rate','Income Level','GDP (Billion USD)','Poverty Rate','Crime count']].corr()
plt.figure(figsize=(10, 6))

# Create a heatmap to visualize the correlation matrix.
sns.heatmap(data=correlation)
plt.title('Correlation Between Economic Factor and Crime Rate')
plt.show()


# Extract the day of the week from the 'Date Rptd' column and add it as a new column 'Day of Week'.
df['Day of Week'] = df['Date Rptd'].dt.day_name()

# Define the specific crime type to filter the data.
crime_types = ['BATTERY - SIMPLE ASSAULT']
# Filter the data to include only the specified crime type.
filtered_data = df[df['Crm Cd Desc'].isin(crime_types)]

# Group the filtered data by the day of the week and count the number of crimes in each day.
crime_by_day = filtered_data.groupby('Day of Week')['DR_NO'].count().reset_index()

plt.figure(figsize=(10, 6))
plt.title('Relationship Between Day of the Week and Crime Frequency')
# Create a bar plot to show the distribution of the specified crime type over the days of the week.
sns.barplot(data = crime_by_day, x='Day of Week', y='DR_NO', order=["Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday"])
plt.xlabel('Day of the Week')
plt.ylabel('Number of Crimes')
plt.show()


# Get the top 5 most frequent crime types.
top_5_crime_types = df['Crm Cd Desc'].value_counts().head(5).index
# Filter the data to include only the top 5 crime types.
filtered_data = df[df['Crm Cd Desc'].isin(top_5_crime_types)]
# Extract the day of the week from the 'Date Rptd' column and add it as a new column 'Day of Week'.
filtered_data['Day of Week'] = filtered_data['Date Rptd'].dt.day_name()

# Group and pivot the data for creating a stacked bar graph
crime_by_day_type = filtered_data.groupby(['Day of Week', 'Crm Cd Desc'])['DR_NO'].count().reset_index()
crime_pivot = crime_by_day_type.pivot(index='Day of Week', columns='Crm Cd Desc', values='DR_NO').fillna(0)
# Define the order of days of the week for the x-axis.
day_order = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']
# Create a stacked bar graph based on the pivoted data.
crime_pivot.loc[day_order].plot(kind='bar', stacked=True)

plt.title('Relationship Between Day of the Week and Top 5 Crime Types Frequency')
plt.xlabel('Day of the Week')
plt.ylabel('Number of Crimes')
plt.xticks(rotation=0)
# Display the legend for the crime types.
plt.legend(title='Crime Types', loc='upper right', bbox_to_anchor=(1.85, 0.3), fontsize="8")
plt.show()


# Get the top 5 most frequent crime types.
top_5_crime_codes = df['Crm Cd Desc'].value_counts().head(5).index.tolist()

# Define the target date to split the data (after May 1st, 2022).
target_date = pd.to_datetime('2021-09-01') # after covid wave

# Separate the data into two periods: before and after the target date.
crime_before = df[df['Date Rptd'] < target_date]
crime_after = df[df['Date Rptd'] >= target_date]

# Filter the data to include only the top 5 crime types for both periods.
crime_before_top_5 = crime_before[crime_before['Crm Cd Desc'].isin(top_5_crime_codes)]
crime_after_top_5 = crime_after[crime_after['Crm Cd Desc'].isin(top_5_crime_codes)]

# Group the data for each crime type separately.
groups_before = crime_before_top_5.groupby('Crm Cd Desc')
groups_after = crime_after_top_5.groupby('Crm Cd Desc')

# Iterate through each crime type to create line plots for occurrences before and after the target date.
for crm_cd, group in groups_before:
    plt.figure(figsize=(10, 6))
    # Group the data by the reporting date and count the occurrences.
    group = group.groupby('Date Rptd')['DR_NO'].count()
    
    # Create a line plot for occurrences before the target date.
    plt.plot(group.index, group.values, label=f'Crm Cd Desc {crm_cd} (Before)')
    plt.title(f'Crime Code Desc {crm_cd} Occurrences Before and After May 1st, 2022')
    plt.xlabel('Date')
    plt.ylabel('Occurrences')
    # Check if there is data for the same crime type after the target date.
    if crm_cd in groups_after.groups:
        group_after = groups_after.get_group(crm_cd).groupby('Date Rptd')['DR_NO'].count()
        # Create a dashed line plot for occurrences after the target date.
        plt.plot(group_after.index, group_after.values, label=f'Crm Cd Desc {crm_cd} (After)', linestyle='--')
    
    plt.legend()
    plt.show()


columns_per_row = 4

# Get the names of the columns to be plotted, starting from the 5th column (index 4).
column_names = df_enc.columns[4:]
num_cols = len(column_names)

# Calculate the number of rows needed to accommodate all columns.
num_rows = (num_cols - 1) // columns_per_row + 1

plt.figure(figsize=(16, 4 * num_rows))

# Iterate through each numerical column and create a boxplot in a subplot.
for i, column in enumerate(column_names):
    plt.subplot(num_rows, columns_per_row, i + 1)
    # plt.hist(df_enc[column], bins=20, color='blue', edgecolor='black')
    # Create a boxplot for the current column.
    plt.boxplot(df_enc[column])
    plt.title(f'Boxplot of {column}')
    plt.xlabel(column)
    plt.ylabel('Frequency')

# Adjust layout and display the subplots
plt.tight_layout()
plt.show()


# Define the list of relevant columns to be extracted.
relevant_columns = ['Vict Age', 'Vict Sex', 'Crm Cd', 'Crm Cd Desc']
# Create a new DataFrame 'data' that includes only the specified columns from the original DataFrame 'df.'
data = df[relevant_columns]


# Age Distribution
plt.figure(figsize=(10, 6))
# Create a histogram to display the distribution of victim ages.
sns.histplot(data['Vict Age'], bins=20, kde=True)
plt.xlabel('Age')
plt.title('Age Distribution')
plt.show()


# Gender Distribution
# Calculate the counts of each gender category in the 'Vict Sex' column.
gender_counts = data['Vict Sex'].value_counts()
plt.figure(figsize=(6, 6))
# Create a pie chart to display the gender distribution of victims.
plt.pie(gender_counts, labels=gender_counts.index, autopct='%1.1f%%')
plt.title('Gender Distribution')
plt.show()


# Crime Type Distribution
plt.figure(figsize=(25, 6))
# Create a countplot to display the distribution of crime types.
sns.countplot(data=data, x='Crm Cd Desc', order=data['Crm Cd Desc'].value_counts().index)
plt.xticks(rotation=90)
plt.xlabel('Crime Type')
plt.ylabel('Count')
plt.title('Crime Type Distribution')
plt.show()


# Define a list of specific crime types to filter the data.
crimes_list = ['ATTEMPTED ROBBERY', 'EXTORTION', 'STALKING']
# Filter the DataFrame to include only data related to the specified crime types.
filtered_df = df[df['Crm Cd Desc'].isin(crimes_list)]

plt.figure(figsize=(12, 6))

# Iterate through each crime type and create a histogram for the age distribution.
for crime_type in crimes_list:
    data1 = filtered_df[filtered_df['Crm Cd Desc'] == crime_type]['Vict Age']
    # Create a histogram for the age distribution, using 20 bins and an alpha value for transparency.
    plt.hist(data1, bins=20, alpha=0.5, label=crime_type)

plt.title('Age vs. Crime Types')
plt.xlabel('Age')
plt.ylabel('Count')
plt.legend()
plt.show()


# Gender vs. Crime Type
# Define a list of specific crime types to filter the data.
crimes_list = ['ATTEMPTED ROBBERY', 'EXTORTION', 'STALKING']
# Filter the DataFrame to include only data related to the specified crime types.
filtered_df = df[df['Crm Cd Desc'].isin(crimes_list)]

# Create a countplot to show the relationship between gender and crime types.
sns.countplot(data=filtered_df, x='Crm Cd Desc', hue='Vict Sex', order=filtered_df['Crm Cd Desc'].value_counts().index)
plt.xlabel('Crime Type')
plt.ylabel('Count')
plt.title('Gender vs. Crime Type')
plt.show()


! pip install prophet

Requirement already satisfied: prophet in c:\users\sivaa\anaconda3\lib\site-packages\prophet-1.1.5-py3.11-win-amd64.egg (1.1.5)
Requirement already satisfied: cmdstanpy>=1.0.4 in c:\users\sivaa\anaconda3\lib\site-packages (from prophet) (1.2.0)
Requirement already satisfied: numpy>=1.15.4 in c:\users\sivaa\anaconda3\lib\site-packages (from prophet) (1.24.3)
Requirement already satisfied: matplotlib>=2.0.0 in c:\users\sivaa\anaconda3\lib\site-packages (from prophet) (3.7.2)
Requirement already satisfied: pandas>=1.0.4 in c:\users\sivaa\anaconda3\lib\site-packages (from prophet) (2.0.3)
Requirement already satisfied: holidays>=0.25 in c:\users\sivaa\anaconda3\lib\site-packages (from prophet) (0.35)
Requirement already satisfied: tqdm>=4.36.1 in c:\users\sivaa\anaconda3\lib\site-packages (from prophet) (4.65.0)
Requirement already satisfied: importlib_resources in c:\users\sivaa\anaconda3\lib\site-packages (from prophet) (6.1.0)
Requirement already satisfied: stanio~=0.3.0 in c:\users\sivaa\anaconda3\lib\site-packages (from cmdstanpy>=1.0.4->prophet) (0.3.0)
Requirement already satisfied: python-dateutil in c:\users\sivaa\anaconda3\lib\site-packages (from holidays>=0.25->prophet) (2.8.2)
Requirement already satisfied: contourpy>=1.0.1 in c:\users\sivaa\anaconda3\lib\site-packages (from matplotlib>=2.0.0->prophet) (1.0.5)
Requirement already satisfied: cycler>=0.10 in c:\users\sivaa\anaconda3\lib\site-packages (from matplotlib>=2.0.0->prophet) (0.11.0)
Requirement already satisfied: fonttools>=4.22.0 in c:\users\sivaa\anaconda3\lib\site-packages\fonttools-4.43.1-py3.11.egg (from matplotlib>=2.0.0->prophet) (4.43.1)
Requirement already satisfied: kiwisolver>=1.0.1 in c:\users\sivaa\anaconda3\lib\site-packages (from matplotlib>=2.0.0->prophet) (1.4.4)
Requirement already satisfied: packaging>=20.0 in c:\users\sivaa\anaconda3\lib\site-packages (from matplotlib>=2.0.0->prophet) (23.1)
Requirement already satisfied: pillow>=6.2.0 in c:\users\sivaa\anaconda3\lib\site-packages (from matplotlib>=2.0.0->prophet) (9.4.0)
Requirement already satisfied: pyparsing<3.1,>=2.3.1 in c:\users\sivaa\anaconda3\lib\site-packages (from matplotlib>=2.0.0->prophet) (3.0.9)
Requirement already satisfied: pytz>=2020.1 in c:\users\sivaa\anaconda3\lib\site-packages (from pandas>=1.0.4->prophet) (2023.3.post1)
Requirement already satisfied: tzdata>=2022.1 in c:\users\sivaa\anaconda3\lib\site-packages (from pandas>=1.0.4->prophet) (2023.3)
Requirement already satisfied: colorama in c:\users\sivaa\anaconda3\lib\site-packages (from tqdm>=4.36.1->prophet) (0.4.6)
Requirement already satisfied: six>=1.5 in c:\users\sivaa\anaconda3\lib\site-packages (from python-dateutil->holidays>=0.25->prophet) (1.16.0)

DEPRECATION: Loading egg at c:\users\sivaa\anaconda3\lib\site-packages\fonttools-4.43.1-py3.11.egg is deprecated. pip 23.3 will enforce this behaviour change. A possible replacement is to use pip for package installation..
DEPRECATION: Loading egg at c:\users\sivaa\anaconda3\lib\site-packages\prophet-1.1.5-py3.11-win-amd64.egg is deprecated. pip 23.3 will enforce this behaviour change. A possible replacement is to use pip for package installation..


# Group the original DataFrame 'df' by 'Date Rptd,' count the occurrences, and reset the index.
df_1=df.groupby('Date Rptd').count().reset_index()[['Date Rptd','DR_NO']]
# Rename the columns to 'ds' (date) and 'y' (count).
df_1.rename(columns={'Date Rptd': 'ds', 'DR_NO': 'y'}, inplace=True)

df_1


from prophet import Prophet

# Create a Prophet model
model = Prophet()

# Fit the model to the historical crime data in 'df_1'
model.fit(df_1)

# Define the forecasting horizon
future = model.make_future_dataframe(periods=365)  # Forecast for one year

# Forecast future crime trends using the fitted model
forecast = model.predict(future)

# Display the last few rows of the forecast to examine the predicted trends
forecast.tail()

18:59:29 - cmdstanpy - INFO - Chain [1] start processing
18:59:29 - cmdstanpy - INFO - Chain [1] done processing


fig = model.plot(forecast)
# Plotting the model
plt.title('Crime Forecast')
plt.show()


# Generate component plots to visualize the model's forecasted components
fig2 = model.plot_components(forecast)
# Display the component plots
plt.show()


from prophet.plot import plot_plotly, plot_components_plotly

# Create an interactive plot for the entire forecast
plot_plotly(model, forecast)

	DR_NO	Date Rptd	DATE OCC	TIME OCC	AREA	AREA NAME	Rpt Dist No	Part 1-2	Crm Cd	Crm Cd Desc	...	Status	Status Desc	Crm Cd 1	Crm Cd 2	Crm Cd 3	Crm Cd 4	LOCATION	Cross Street	LAT	LON
0	10304468	01/08/2020 12:00:00 AM	01/08/2020 12:00:00 AM	2230	3	Southwest	377	2	624	BATTERY - SIMPLE ASSAULT	...	AO	Adult Other	624.0	NaN	NaN	NaN	1100 W 39TH PL	NaN	34.0141	-118.2978
1	190101086	01/02/2020 12:00:00 AM	01/01/2020 12:00:00 AM	330	1	Central	163	2	624	BATTERY - SIMPLE ASSAULT	...	IC	Invest Cont	624.0	NaN	NaN	NaN	700 S HILL ST	NaN	34.0459	-118.2545
2	200110444	04/14/2020 12:00:00 AM	02/13/2020 12:00:00 AM	1200	1	Central	155	2	845	SEX OFFENDER REGISTRANT OUT OF COMPLIANCE	...	AA	Adult Arrest	845.0	NaN	NaN	NaN	200 E 6TH ST	NaN	34.0448	-118.2474
3	191501505	01/01/2020 12:00:00 AM	01/01/2020 12:00:00 AM	1730	15	N Hollywood	1543	2	745	VANDALISM - MISDEAMEANOR ($399 OR UNDER)	...	IC	Invest Cont	745.0	998.0	NaN	NaN	5400 CORTEEN PL	NaN	34.1685	-118.4019
4	191921269	01/01/2020 12:00:00 AM	01/01/2020 12:00:00 AM	415	19	Mission	1998	2	740	VANDALISM - FELONY ($400 & OVER, ALL CHURCH VA...	...	IC	Invest Cont	740.0	NaN	NaN	NaN	14400 TITUS ST	NaN	34.2198	-118.4468
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
807372	231604807	01/27/2023 12:00:00 AM	01/26/2023 12:00:00 AM	1800	16	Foothill	1663	2	740	VANDALISM - FELONY ($400 & OVER, ALL CHURCH VA...	...	IC	Invest Cont	740.0	NaN	NaN	NaN	12500 BRANFORD ST	NaN	34.2466	-118.4054
807373	231606525	03/22/2023 12:00:00 AM	03/22/2023 12:00:00 AM	1000	16	Foothill	1602	1	230	ASSAULT WITH DEADLY WEAPON, AGGRAVATED ASSAULT	...	IC	Invest Cont	230.0	NaN	NaN	NaN	12800 FILMORE ST	NaN	34.2790	-118.4116
807374	231210064	04/12/2023 12:00:00 AM	04/12/2023 12:00:00 AM	1630	12	77th Street	1239	1	230	ASSAULT WITH DEADLY WEAPON, AGGRAVATED ASSAULT	...	IC	Invest Cont	230.0	NaN	NaN	NaN	6100 S VERMONT AV	NaN	33.9841	-118.2915
807375	230115220	07/02/2023 12:00:00 AM	07/01/2023 12:00:00 AM	1	1	Central	154	1	352	PICKPOCKET	...	IC	Invest Cont	352.0	NaN	NaN	NaN	500 S MAIN ST	NaN	34.0467	-118.2485
807376	230906458	03/05/2023 12:00:00 AM	03/05/2023 12:00:00 AM	900	9	Van Nuys	914	2	745	VANDALISM - MISDEAMEANOR ($399 OR UNDER)	...	IC	Invest Cont	745.0	NaN	NaN	NaN	14500 HARTLAND ST	NaN	34.1951	-118.4487

	DR_NO	Date Rptd	DATE OCC	TIME OCC	AREA	AREA NAME	Rpt Dist No	Part 1-2	Crm Cd	Crm Cd Desc	...	Status	Status Desc	Crm Cd 1	Crm Cd 2	Crm Cd 3	Crm Cd 4	LOCATION	Cross Street	LAT	LON
0	10304468	01/08/2020 12:00:00 AM	01/08/2020 12:00:00 AM	2230	3	Southwest	377	2	624	BATTERY - SIMPLE ASSAULT	...	AO	Adult Other	624.0	NaN	NaN	NaN	1100 W 39TH PL	NaN	34.0141	-118.2978
1	190101086	01/02/2020 12:00:00 AM	01/01/2020 12:00:00 AM	330	1	Central	163	2	624	BATTERY - SIMPLE ASSAULT	...	IC	Invest Cont	624.0	NaN	NaN	NaN	700 S HILL ST	NaN	34.0459	-118.2545
2	200110444	04/14/2020 12:00:00 AM	02/13/2020 12:00:00 AM	1200	1	Central	155	2	845	SEX OFFENDER REGISTRANT OUT OF COMPLIANCE	...	AA	Adult Arrest	845.0	NaN	NaN	NaN	200 E 6TH ST	NaN	34.0448	-118.2474
3	191501505	01/01/2020 12:00:00 AM	01/01/2020 12:00:00 AM	1730	15	N Hollywood	1543	2	745	VANDALISM - MISDEAMEANOR ($399 OR UNDER)	...	IC	Invest Cont	745.0	998.0	NaN	NaN	5400 CORTEEN PL	NaN	34.1685	-118.4019
4	191921269	01/01/2020 12:00:00 AM	01/01/2020 12:00:00 AM	415	19	Mission	1998	2	740	VANDALISM - FELONY ($400 & OVER, ALL CHURCH VA...	...	IC	Invest Cont	740.0	NaN	NaN	NaN	14400 TITUS ST	NaN	34.2198	-118.4468

	DR_NO	TIME OCC	AREA	Rpt Dist No	Part 1-2	Crm Cd	Vict Age	Premis Cd	Weapon Used Cd	Crm Cd 1	Crm Cd 2	Crm Cd 3	Crm Cd 4	LAT	LON
count	8.073770e+05	807377.000000	807377.000000	807377.000000	807377.000000	807377.000000	807377.000000	807368.000000	281174.000000	807367.000000	59483.000000	1987.000000	58.000000	807377.000000	807377.000000
mean	2.158850e+08	1335.489603	10.713686	1117.794578	1.414134	500.748725	29.838388	305.740185	362.876077	500.487070	957.481028	983.894313	990.500000	33.978009	-118.022322
std	1.074677e+07	654.144480	6.092573	609.246761	0.492572	207.825396	21.766978	216.586242	123.724210	207.614181	111.512248	50.749706	28.359998	1.804822	6.258038
min	8.170000e+02	1.000000	1.000000	101.000000	1.000000	110.000000	-3.000000	101.000000	101.000000	110.000000	210.000000	434.000000	821.000000	0.000000	-118.667600
25%	2.101192e+08	900.000000	6.000000	622.000000	1.000000	331.000000	8.000000	101.000000	310.000000	331.000000	998.000000	998.000000	998.000000	34.013600	-118.429600
50%	2.201087e+08	1415.000000	11.000000	1142.000000	1.000000	442.000000	31.000000	203.000000	400.000000	442.000000	998.000000	998.000000	998.000000	34.058400	-118.321500
75%	2.219051e+08	1900.000000	16.000000	1617.000000	2.000000	626.000000	45.000000	501.000000	400.000000	626.000000	998.000000	998.000000	998.000000	34.163100	-118.273900
max	2.399165e+08	2359.000000	21.000000	2199.000000	2.000000	956.000000	120.000000	976.000000	516.000000	956.000000	999.000000	999.000000	999.000000	34.334300	0.000000

	DR_NO	Date Rptd	DATE OCC	TIME OCC	AREA	AREA NAME	Rpt Dist No	Part 1-2	Crm Cd	Crm Cd Desc	...	Premis Cd	Premis Desc	Weapon Used Cd	Weapon Desc	Status	Status Desc	Crm Cd 1	LOCATION	LAT	LON
0	10304468	01/08/2020 12:00:00 AM	01/08/2020 12:00:00 AM	2230	3	Southwest	377	2	624	BATTERY - SIMPLE ASSAULT	...	501.0	SINGLE FAMILY DWELLING	400.000000	STRONG-ARM (HANDS, FIST, FEET OR BODILY FORCE)	AO	Adult Other	624.0	1100 W 39TH PL	34.0141	-118.2978
1	190101086	01/02/2020 12:00:00 AM	01/01/2020 12:00:00 AM	330	1	Central	163	2	624	BATTERY - SIMPLE ASSAULT	...	102.0	SIDEWALK	500.000000	UNKNOWN WEAPON/OTHER WEAPON	IC	Invest Cont	624.0	700 S HILL ST	34.0459	-118.2545
2	200110444	04/14/2020 12:00:00 AM	02/13/2020 12:00:00 AM	1200	1	Central	155	2	845	SEX OFFENDER REGISTRANT OUT OF COMPLIANCE	...	726.0	POLICE FACILITY	362.876077	STRONG-ARM (HANDS, FIST, FEET OR BODILY FORCE)	AA	Adult Arrest	845.0	200 E 6TH ST	34.0448	-118.2474
3	191501505	01/01/2020 12:00:00 AM	01/01/2020 12:00:00 AM	1730	15	N Hollywood	1543	2	745	VANDALISM - MISDEAMEANOR ($399 OR UNDER)	...	502.0	MULTI-UNIT DWELLING (APARTMENT, DUPLEX, ETC)	362.876077	STRONG-ARM (HANDS, FIST, FEET OR BODILY FORCE)	IC	Invest Cont	745.0	5400 CORTEEN PL	34.1685	-118.4019
4	191921269	01/01/2020 12:00:00 AM	01/01/2020 12:00:00 AM	415	19	Mission	1998	2	740	VANDALISM - FELONY ($400 & OVER, ALL CHURCH VA...	...	409.0	BEAUTY SUPPLY STORE	362.876077	STRONG-ARM (HANDS, FIST, FEET OR BODILY FORCE)	IC	Invest Cont	740.0	14400 TITUS ST	34.2198	-118.4468
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
807372	231604807	01/27/2023 12:00:00 AM	01/26/2023 12:00:00 AM	1800	16	Foothill	1663	2	740	VANDALISM - FELONY ($400 & OVER, ALL CHURCH VA...	...	122.0	VEHICLE, PASSENGER/TRUCK	362.876077	STRONG-ARM (HANDS, FIST, FEET OR BODILY FORCE)	IC	Invest Cont	740.0	12500 BRANFORD ST	34.2466	-118.4054
807373	231606525	03/22/2023 12:00:00 AM	03/22/2023 12:00:00 AM	1000	16	Foothill	1602	1	230	ASSAULT WITH DEADLY WEAPON, AGGRAVATED ASSAULT	...	102.0	SIDEWALK	400.000000	STRONG-ARM (HANDS, FIST, FEET OR BODILY FORCE)	IC	Invest Cont	230.0	12800 FILMORE ST	34.2790	-118.4116
807374	231210064	04/12/2023 12:00:00 AM	04/12/2023 12:00:00 AM	1630	12	77th Street	1239	1	230	ASSAULT WITH DEADLY WEAPON, AGGRAVATED ASSAULT	...	222.0	LAUNDROMAT	500.000000	UNKNOWN WEAPON/OTHER WEAPON	IC	Invest Cont	230.0	6100 S VERMONT AV	33.9841	-118.2915
807375	230115220	07/02/2023 12:00:00 AM	07/01/2023 12:00:00 AM	1	1	Central	154	1	352	PICKPOCKET	...	735.0	NIGHT CLUB (OPEN EVENINGS ONLY)	362.876077	STRONG-ARM (HANDS, FIST, FEET OR BODILY FORCE)	IC	Invest Cont	352.0	500 S MAIN ST	34.0467	-118.2485
807376	230906458	03/05/2023 12:00:00 AM	03/05/2023 12:00:00 AM	900	9	Van Nuys	914	2	745	VANDALISM - MISDEAMEANOR ($399 OR UNDER)	...	502.0	MULTI-UNIT DWELLING (APARTMENT, DUPLEX, ETC)	362.876077	STRONG-ARM (HANDS, FIST, FEET OR BODILY FORCE)	IC	Invest Cont	745.0	14500 HARTLAND ST	34.1951	-118.4487

	DR_NO	Date Rptd	DATE OCC	TIME OCC	AREA	AREA NAME	Rpt Dist No	Part 1-2	Crm Cd	Crm Cd Desc	...	Premis Cd	Premis Desc	Weapon Used Cd	Weapon Desc	Status	Status Desc	Crm Cd 1	LOCATION	LAT	LON
0	10304468	2020-01-08	2020-01-08	22:30:00	3	Southwest	377	2	624	BATTERY - SIMPLE ASSAULT	...	501	SINGLE FAMILY DWELLING	400	STRONG-ARM (HANDS, FIST, FEET OR BODILY FORCE)	AO	Adult Other	624	1100 W 39TH PL	34.0141	-118.2978
1	190101086	2020-01-02	2020-01-01	03:30:00	1	Central	163	2	624	BATTERY - SIMPLE ASSAULT	...	102	SIDEWALK	500	UNKNOWN WEAPON/OTHER WEAPON	IC	Invest Cont	624	700 S HILL ST	34.0459	-118.2545
2	200110444	2020-04-14	2020-02-13	12:00:00	1	Central	155	2	845	SEX OFFENDER REGISTRANT OUT OF COMPLIANCE	...	726	POLICE FACILITY	362	STRONG-ARM (HANDS, FIST, FEET OR BODILY FORCE)	AA	Adult Arrest	845	200 E 6TH ST	34.0448	-118.2474
3	191501505	2020-01-01	2020-01-01	17:30:00	15	N Hollywood	1543	2	745	VANDALISM - MISDEAMEANOR ($399 OR UNDER)	...	502	MULTI-UNIT DWELLING (APARTMENT, DUPLEX, ETC)	362	STRONG-ARM (HANDS, FIST, FEET OR BODILY FORCE)	IC	Invest Cont	745	5400 CORTEEN PL	34.1685	-118.4019
4	191921269	2020-01-01	2020-01-01	04:15:00	19	Mission	1998	2	740	VANDALISM - FELONY ($400 & OVER, ALL CHURCH VA...	...	409	BEAUTY SUPPLY STORE	362	STRONG-ARM (HANDS, FIST, FEET OR BODILY FORCE)	IC	Invest Cont	740	14400 TITUS ST	34.2198	-118.4468
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
807372	231604807	2023-01-27	2023-01-26	18:00:00	16	Foothill	1663	2	740	VANDALISM - FELONY ($400 & OVER, ALL CHURCH VA...	...	122	VEHICLE, PASSENGER/TRUCK	362	STRONG-ARM (HANDS, FIST, FEET OR BODILY FORCE)	IC	Invest Cont	740	12500 BRANFORD ST	34.2466	-118.4054
807373	231606525	2023-03-22	2023-03-22	10:00:00	16	Foothill	1602	1	230	ASSAULT WITH DEADLY WEAPON, AGGRAVATED ASSAULT	...	102	SIDEWALK	400	STRONG-ARM (HANDS, FIST, FEET OR BODILY FORCE)	IC	Invest Cont	230	12800 FILMORE ST	34.2790	-118.4116
807374	231210064	2023-04-12	2023-04-12	16:30:00	12	77th Street	1239	1	230	ASSAULT WITH DEADLY WEAPON, AGGRAVATED ASSAULT	...	222	LAUNDROMAT	500	UNKNOWN WEAPON/OTHER WEAPON	IC	Invest Cont	230	6100 S VERMONT AV	33.9841	-118.2915
807375	230115220	2023-07-02	2023-07-01	00:01:00	1	Central	154	1	352	PICKPOCKET	...	735	NIGHT CLUB (OPEN EVENINGS ONLY)	362	STRONG-ARM (HANDS, FIST, FEET OR BODILY FORCE)	IC	Invest Cont	352	500 S MAIN ST	34.0467	-118.2485
807376	230906458	2023-03-05	2023-03-05	09:00:00	9	Van Nuys	914	2	745	VANDALISM - MISDEAMEANOR ($399 OR UNDER)	...	502	MULTI-UNIT DWELLING (APARTMENT, DUPLEX, ETC)	362	STRONG-ARM (HANDS, FIST, FEET OR BODILY FORCE)	IC	Invest Cont	745	14500 HARTLAND ST	34.1951	-118.4487

Data Acquisition¶

Data Inspection¶

Data Cleaning¶

Exploratory Data Analysis¶

Overall Crime Trends¶

Seasonal Patterns¶

Most Common Crime Type:¶

Regional Differences¶

Correlation with Economic Factors:¶

Day of the Week Analysis¶

Investigate any impact of major events or policy changes on crime rates¶

Outliers and Anomalies¶

Demographic Factors¶

Advanced Analysis¶

	DR_NO	Date Rptd	DATE OCC	TIME OCC	AREA	AREA NAME	Rpt Dist No	Part 1-2	Crm Cd	Crm Cd Desc	...	Premis Cd	Premis Desc	Weapon Used Cd	Weapon Desc	Status	Status Desc	Crm Cd 1	LOCATION	LAT	LON
0	0.042947	2020-01-08	2020-01-08	22:30:00	0.10	15	0.131554	1.0	0.607565	4	...	0.457143	255	0.720482	65	1	1	0.607565	5134	34.0141	-118.2978
1	0.792363	2020-01-02	2020-01-01	03:30:00	0.00	1	0.029552	1.0	0.607565	4	...	0.001143	254	0.961446	75	3	2	0.607565	46427	34.0459	-118.2545
2	0.834083	2020-04-14	2020-02-13	12:00:00	0.00	1	0.025739	1.0	0.868794	100	...	0.714286	235	0.628916	65	0	0	0.868794	21922	34.0448	-118.2474
3	0.798200	2020-01-01	2020-01-01	17:30:00	0.70	8	0.687321	1.0	0.750591	130	...	0.458286	207	0.628916	65	3	2	0.750591	40378	34.1685	-118.4019
4	0.799950	2020-01-01	2020-01-01	04:15:00	0.90	7	0.904194	1.0	0.744681	129	...	0.352000	23	0.628916	65	3	2	0.744681	13396	34.2198	-118.4468
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
807372	0.965356	2023-01-27	2023-01-26	18:00:00	0.75	3	0.744519	1.0	0.744681	129	...	0.024000	298	0.628916	65	3	2	0.744681	9088	34.2466	-118.4054
807373	0.965363	2023-03-22	2023-03-22	10:00:00	0.75	3	0.715443	0.0	0.141844	2	...	0.001143	254	0.720482	65	3	2	0.141844	9608	34.2790	-118.4116
807374	0.963710	2023-04-12	2023-04-12	16:30:00	0.55	0	0.542421	0.0	0.141844	2	...	0.138286	101	0.961446	75	3	2	0.141844	43521	33.9841	-118.2915
807375	0.959147	2023-07-02	2023-07-01	00:01:00	0.00	1	0.025262	0.0	0.286052	88	...	0.724571	212	0.628916	65	3	2	0.286052	38717	34.0467	-118.2485
807376	0.962445	2023-03-05	2023-03-05	09:00:00	0.40	17	0.387512	1.0	0.750591	130	...	0.458286	207	0.628916	65	3	2	0.750591	13449	34.1951	-118.4487

	Date	Unemployment Rate	Income Level	GDP (Billion USD)	Poverty Rate	Crime count
0	08-01-2020	0.159	53800.0	1090	0.153	18478.0
1	08-02-2020	0.158	53900.0	1100	0.152	17252.0
2	08-03-2020	0.157	54000.0	1110	0.151	16157.0
3	08-04-2020	0.156	54100.0	1120	0.150	15676.0
4	08-05-2020	0.155	54200.0	1130	0.149	17200.0
5	08-06-2020	0.154	54300.0	1140	0.148	17028.0
6	08-07-2020	0.153	54400.0	1150	0.147	17112.0
7	08-08-2020	0.152	54500.0	1160	0.146	16870.0
8	08-09-2020	0.151	54600.0	1170	0.145	15629.0
9	08-10-2020	0.150	54700.0	1180	0.144	16480.0
10	08-11-2020	0.149	54800.0	1190	0.143	15565.0
11	08-12-2020	0.148	54900.0	1200	0.142	15937.0
12	08-01-2021	0.147	55000.0	1210	0.141	16531.0
13	08-02-2021	0.146	55100.0	1220	0.140	15403.0
14	08-03-2021	0.145	55200.0	1230	0.139	16313.0
15	08-04-2021	0.144	55300.0	1240	0.138	16047.0
16	08-05-2021	0.143	55400.0	1250	0.137	16985.0
17	08-06-2021	0.142	55500.0	1260	0.136	17144.0
18	08-07-2021	0.141	55600.0	1270	0.135	18654.0
19	08-08-2021	0.140	55700.0	1280	0.134	18365.0
20	08-09-2021	0.139	55800.0	1290	0.133	18347.0
21	08-10-2021	0.138	55900.0	1300	0.132	19286.0
22	08-11-2021	0.137	56000.0	1310	0.131	18327.0
23	08-12-2021	0.136	56100.0	1320	0.130	17911.0
24	08-01-2022	0.135	56200.0	1330	0.129	18426.0
25	08-02-2022	0.134	56300.0	1340	0.128	17687.0
26	08-03-2022	0.133	56400.0	1350	0.127	19682.0
27	08-04-2022	0.125	56000.0	1300	0.132	19777.0
28	08-05-2022	0.123	56100.0	1310	0.131	20398.0
29	08-06-2022	0.121	56200.0	1320	0.130	20185.0
30	08-07-2022	0.119	56300.0	1330	0.129	19931.0
31	08-08-2022	0.117	56400.0	1340	0.128	20060.0
32	08-09-2022	0.115	56500.0	1350	0.127	19251.0
33	08-10-2022	0.113	56600.0	1360	0.126	20221.0
34	08-11-2022	0.111	56700.0	1370	0.125	18646.0
35	08-12-2022	0.109	56800.0	1380	0.124	19880.0
36	08-01-2023	0.107	56900.0	1390	0.123	19548.0
37	08-02-2023	0.105	57000.0	1400	0.122	18239.0
38	08-03-2023	0.103	57100.0	1410	0.121	18961.0
39	08-04-2023	0.101	57200.0	1420	0.120	18672.0
40	03-05-2023	0.099	57300.0	1430	0.119	18627.0
41	08-05-2023	0.099	57300.0	1430	0.119	18362.0
42	08-06-2023	0.097	57400.0	1440	0.118	19475.0
43	08-07-2023	0.095	57500.0	1450	0.117	19173.0
44	08-08-2023	0.093	57600.0	1460	0.116	13479.0
45	08-09-2023	0.091	57700.0	1470	0.115	NaN

	ds	y
0	2020-01-01	354
1	2020-01-02	398
2	2020-01-03	403
3	2020-01-04	436
4	2020-01-05	420
...	...	...
1359	2023-09-21	582
1360	2023-09-22	610
1361	2023-09-23	537
1362	2023-09-24	474
1363	2023-09-25	389

	ds	trend	yhat_lower	yhat_upper	trend_lower	trend_upper	additive_terms	additive_terms_lower	additive_terms_upper	weekly	weekly_lower	weekly_upper	yearly	yearly_lower	yearly_upper	yhat
1724	2024-09-20	597.850466	527.636988	664.937401	577.295072	621.563307	0.658088	0.658088	0.658088	0.605225	0.605225	0.605225	0.052863	0.052863	0.052863	598.508554
1725	2024-09-21	597.774129	470.101424	604.555943	577.053714	621.552690	-58.419840	-58.419840	-58.419840	-58.450749	-58.450749	-58.450749	0.030909	0.030909	0.030909	539.354289
1726	2024-09-22	597.697792	460.210886	601.432876	576.828497	621.588142	-66.590787	-66.590787	-66.590787	-66.654857	-66.654857	-66.654857	0.064069	0.064069	0.064069	531.107005
1727	2024-09-23	597.621455	584.551759	731.523448	576.582060	621.606249	59.688997	59.688997	59.688997	59.542691	59.542691	59.542691	0.146306	0.146306	0.146306	657.310452
1728	2024-09-24	597.545118	568.819289	698.334327	576.354746	621.652048	33.004824	33.004824	33.004824	32.733242	32.733242	32.733242	0.271583	0.271583	0.271583	630.549943